Author

A. Krasoulis

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 5

Signature Assays, Organisms & Methods

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Assay:electrophysiology· 4 Research Focus:benchmarking· 2 Approach:deep learning· 2 Approach:experimental assay· 2 Organism:human· 2 Approach:machine learning (classical)· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1

Frequent Co-Authors

K. Nazarpour· 5 shared S. Vijayakumar· 2 shared A. Ganguly· 1 shared A. J. van den Bogert· 1 shared D. Blana· 1 shared E. K. Chadwick· 1 shared W. M. Murray· 1 shared

Activity by Year

2019-2020 · peak 3/yr

Co-Author Collaboration Network

7 top collaborators · 16 mutual co-author links

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5 shared papersBioengineering

K. Nazarpour

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Author profile →

5 papers

Recent·Most cited

Discrete action control for prosthetic digits

A. Krasoulis, K. Nazarpour

This paper introduces action control for prosthetic digits using EMG, showing significant performance improvements over position control in both task and non-task measures.

bioRxiv · Mar 25, 2020Bioengineering

Myoelectric digit action decoding with multi-label, multi-class classification: an offline analysis

A. Krasoulis, K. Nazarpour

This paper proposes a multi-label, multi-class classification approach for decoding myoelectric digit actions from EMG signals and demonstrates its feasibility using a public dataset.

bioRxiv · Mar 24, 2020Bioengineering

Multi-grip classification-based prosthesis control with two EMG-IMU sensors

A. Krasoulis, S. Vijayakumar, K. Nazarpour

This paper proposes a two-sensor system for multi-grip prosthesis control using EMG and IMU data, with confidence-based error rejection to improve reliability.

bioRxiv · Dec 9, 2019Bioengineering

Effect of user adaptation on prosthetic finger control with an intuitive myoelectric decoder

A. Krasoulis, S. Vijayakumar, K. Nazarpour

The paper investigates how user adaptation affects myoelectric control performance in prosthetic finger control using an intuitive decoder, finding that experience leads to significant performance improvements.

bioRxiv · Aug 9, 2019Bioengineering

Model-based control of individual finger movements for prosthetic hand function

D. Blana, A. J. van den Bogert, W. M. Murray, A. Ganguly, A. Krasoulis, K. Nazarpour, E. K. Chadwick

This study presents a biomechanical model for prosthetic hand control, demonstrating its ability to replicate hand movements and enable real-time control using EMG signals.

bioRxiv · May 7, 2019Bioengineering